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As explained in these introduction to random intercepts models lesson, for a simple random intercepts model, the model definition is the following: $$y_{ij} = b_0+b_1x_{ij}+u_j+e_{ij}$$ Where $y_{ij}$ is the response of the individual $i$ which is in group $j$, $b_0$ is the intercept, $b_1$ the slope, $x_{ij}$ the predictor for that individual, $u_j$ the random intercept corresponding to group $j$ and $e_{ij}$ a random noise.

My question is: is this model equivalent to a linear model with more variables, $x$ and a dummy variable for every group? And more generally, can every mixed model be transformed into a generalized linear model?

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  • $\begingroup$ It will depend on the distribution you specify for the error terms $e_{ij}$. $\endgroup$ – Dimitris Rizopoulos Jun 14 '19 at 10:36
  • $\begingroup$ If they are $N(0, \sigma^2)$, is it the same though? In which case are they different? $\endgroup$ – David Masip Jun 14 '19 at 11:03
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    $\begingroup$ You could for example assume that the error terms have a t distribution. More specifically, this is not just a linear regression with more variables. It’s a linear mixed model. The random effect $u$ is an unobserved variable. When you define the model for the observed data $y$ alone, integrating over $u$, you get a multivariate regression model with correlated error terms. $\endgroup$ – Dimitris Rizopoulos Jun 14 '19 at 21:40
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This is not a generalized linear model (well, technically it is), it is just a plain old linear model!

You can define a dummy variable for each of the groups (except one "reference group") You can then build your individuals-variables matrix as in any linear model, and solve by least-squares to estimate the parameters.

EDIT: I am not sure about what exactly counts as a "mixed model". Many of those names are used for historical reasons, as the general formulation of the linear model has only been around for about half a century (It is confusing, but please don't mistake "general linear model" with "generalized linear model". The former refers to any model than can be written as $Y=AX+\epsilon$ while the latter also allows for some transformations that make it suitable for other problems like classification (logistic regression is probably the most famous case of a generalized linear model)

Another reason why those specific names exist is clarity in description, as, although mathematically the model you describe is just "multivariant regression", from a "functional" point of view, it's quite different (those "dummy variables" are just a trick we use to make it fit into the linear model form, but we don't often think of cateogrical variables as their dummy representation)

Finally, I guess pretending we are working with a set of different models, rather than a general one, makes everyone else think we are smarter!

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  • $\begingroup$ So they are the same thing in this case, right? $\endgroup$ – David Masip Jun 14 '19 at 10:19
  • $\begingroup$ Yes. What you have in your hands is, from an algrebraic perspective, no different that multiple linear regression $\endgroup$ – David Jun 14 '19 at 10:23
  • $\begingroup$ And is this true for any mixed model? And if so, where is the need to provide cool names such as random intercepts while they are doing a linear model? $\endgroup$ – David Masip Jun 14 '19 at 10:25
  • $\begingroup$ @DavidMasip The answer may be a bit complex, so I'll edit my original response $\endgroup$ – David Jun 14 '19 at 10:32
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    $\begingroup$ Part of this answer is not actually correct; the $u_j$ in a random intercept model can't be estimated using dummy variables (that would be a fixed-effects model.) The link in the OP makes this clear. However, you are correct that it is not a generalized linear model. $\endgroup$ – jbowman Jun 14 '19 at 14:52

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